MACHINE LEARNING TECHNIQUES FOR DETECTINGDATA BREACHES IN CLOUD-BASED SYSTEM

Abstract

The increasing adoption of cloud-based systems has intensified concerns regarding data breaches, necessitating the development of intelligent and adaptive security mechanisms. This study examines the application of machine learning techniques for detecting data breaches in cloud environments, focusing on their effectiveness in addressing the complexities of large-scale, dynamic, and distributed infrastructures. The review explores key concepts, including machine learning paradigms, cloud computing architectures, and the nature of data breaches, while analysing contemporary approaches to intrusion and breach detection. Empirical findings indicate that supervised and unsupervised learning models achieve detection accuracies exceeding 90%, with hybrid and ensemble techniques further enhancing performance and reducing false positive rates. The study highlights the role of anomaly detection and behavioural analysis in identifying irregular system activities and insider threats, which account for a significant proportion of cloud-related security incidents. Additionally, the research examines critical cloud security challenges, including misconfigurations, identity and access management weaknesses, and multi-tenancy vulnerabilities, which continue to expose sensitive data to unauthorised access. Machine learning-driven solutions demonstrate the ability to process high-volume data streams, enabling real-time monitoring and improved incident response. The integration of advanced models, including deep learning and ensemble approaches, contributes to more robust and scalable security frameworks. However, issues related to data quality, model interpretability, and evolving threat patterns remain significant considerations in the deployment of these techniques. The study underscores the importance of adaptive and intelligent detection systems in strengthening cloud security and mitigating the impact of data breaches in modern computing environments